In traditional supply chains, material trickles downstream sequentially while data sequentially moves back upstream. With information latency between the touch points in such a supply chain, excess inventory may pile up at points along a supply chain as buffers against supply and demand forecasts that may not be accurate. Such inventory carrying costs reduce profits and increase order-to-delivery and cash-to-cash cycles. Furthermore, disconnections between inventory flow and information concerning that inventory flow exacerbate problems associated with proactively managing inventory in real-time or near real-time.
A supply chain may include multiple enterprises that need to communicate and cooperate to insure that goods and/or services are moved from suppliers to consumers in a timely, efficient manner. Conventionally, supply chain data is stored in a series of databases that are individually owned, controlled and/or designed. Users of such supply chain databases typically custom craft display screens (e.g., browser based user interfaces) and/or hard copy formats to correspond to their understanding of the database(s) and their desired outputs. Such custom crafted screens and/or printouts may rely on an incomplete and/or incorrect understanding of the data in the database(s). Furthermore, such custom crafted display solutions may be difficult to program, even more difficult to reprogram, and even more difficult still to integrate with other custom crafted display solutions and databases, due, for example, to inconsistent formats between supply chain member data.
Conventionally, an enterprise may be a member of many supply chains. For example, a widget producer may ship widgets to a variety of sources including widget assemblers, widget customizers, widget testers, widget wholesalers, widget retailers and widget consumers. Furthermore, a widget may arrive at its ultimate destination after transiting multiple supply chains. For example, a first widget consumer may receive a widget directly from a widget producer, a second widget consumer may receive a widget via a widget retailer who received it from a widget wholesaler, and a third widget consumer may receive a widget via a widget customizer and widget tester. Different points in different supply chains may employ different forms, data and/or customs to achieve delivery and facilitate record keeping. Thus, one widget supplier may have to maintain multiple supply chain protocols, forms and/or records to communicate with members of the various supply chains of which it is a member. This increases supply chain data processing complexity and thus reduces efficiency in supply chain operations. As contract manufacturing, dedicated suppliers and vendor managed inventories increase, such data processing problems are exacerbated due to the expanding network of enterprises included in a supply chain.
Conventionally, members of a supply chain keep individual records concerning their enterprises. By way of illustration, a supply chain member may keep records concerning items including, but not limited to, purchase orders sent, purchase orders received, inventory, sales orders sent, sales orders received, warehouse orders, shipments, and the like. Such records may be stored in one or more individually owned, controlled and/or designed databases and may be stored in inconsistent formats. Typically, due to security concerns, supply chain members do not open their databases to access from other supply chain members. Thus, to communicate with other supply chain members it has been customary to exchange EDI (Electronic Data Interchange) data and/or paper printouts. Producing, shipping and interpreting both EDI and paper printouts introduces delays and potential points of confusion into supply chain processing. Furthermore, EDI and/or paper employed in one supply chain may not be interchangeable with other supply chains, creating additional complexity in supply chain data processing.
Conventionally, supply chain members deploy inventory between facilities based on projections concerning supply and demand. Such projections may be based on models that account for a number of factors including historical demand, historical supply, phoned in updates and inventory manager intuition. However, such projections become stale as soon as they are issued since they do not rely on the actual inventory, supply and/or demand situation at facilities. The projections grow increasingly more stale as a reporting period progresses. For example, a report generated for a one month long reporting cycle may initially be acceptable. However, changing conditions in the field (e.g., warehouse fire, run on supply at a location, work action slowdown) may make the report unacceptably inaccurate. Typically, there were limited, if any, means to adapt inventory distributions within a reporting cycle.